added json, gallery, dataframe

app.py

@@ -251,9 +251,10 @@ MD_model = torch.hub.load('ultralytics/yolov5', 'custom', "megadet_model/md_v5b.
 
 
 ####################################################
-# Create user interface and launch
+# Create user interface and launch: all inputs
 gr_image_input = gr.inputs.Image(type="pil", label="Input Image")
-gr_image_output = gr.outputs.Image(type="pil", label="Output Image")
+
+
 gr_dlc_model_input = gr.inputs.Dropdown(choices=['full_cat','full_dog', 'monkey_face', 'full_human', 'full_monkey'], # choices
                                         default='full_cat', # default option
                                         type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
@@ -274,12 +275,29 @@ gr_pose_font_input = gr.inputs.Dropdown(choices=['amiko', 'nature', 'painter', '
                                         label='Select pose font')
 gr_slider_font_size = gr.inputs.Slider(5,30,1,8,
                                         label='Set pose font size')                                        
-                                        
+
+inputs = [gr_image_input,
+                     gr_dlc_model_input,
+                     gr_dlc_only_checkbox,
+                     gr_slider_conf_bboxes,
+                     gr_slider_conf_keypoints,
+                     gr_keypt_color,
+                     gr_pose_font_input,
+                     gr_slider_font_size, 
+                     ]                                                                                
 
 #image = gr.inputs.Image(type="pil", label="Input Image")
 #chosen_model = gr.inputs.Dropdown(choices = models, value = "model_weights/md_v5a.0.0.pt",type = "value", label="Model Weight")
 #size = 640
 
+####################################################
+# Create user interface and launch: all outputs
+gr_gallery_output = gr.outputs.Gallery(type="pil", label="Output Gallery")
+gr_json_output = gr.outputs.json(label='megadetector json')
+gr_json_output = gr.outputs.dataframe(label='pose coordinates')
+
+outputs = [gr_gallery_output, gr_json_output, gr_json_output]
+
 gr_title = "MegaDetector v5 + DLClive"
 gr_description = "Detect and estimate the pose of animals in camera trap images, using MegaDetector v5a + DeepLabCut-live. \
                   Builds up on work from <a href='https://huggingface.co/spaces/hlydecker/MegaDetector_v5'>hlydecker/MegaDetector_v5</a>"
@@ -287,16 +305,8 @@ gr_description = "Detect and estimate the pose of animals in camera trap images,
 # examples = [['data/Macropod.jpg'], ['data/koala2.jpg'],['data/cat.jpg'],['data/BrushtailPossum.jpg']]
 
 demo = gr.Interface(predict_pipeline, 
-             inputs=[gr_image_input,
-                     gr_dlc_model_input,
-                     gr_dlc_only_checkbox,
-                     gr_slider_conf_bboxes,
-                     gr_slider_conf_keypoints,
-                     gr_keypt_color,
-                     gr_pose_font_input,
-                     gr_slider_font_size, 
-                     ], 
-             outputs=gr_image_output, 
+             inputs=inputs,
+             outputs=outputs, 
              title=gr_title, 
              description=gr_description,
              theme="huggingface",